DEC

Top Publishers

data_set2 <- data_set %>% gather("Region","Sales",c("NA_Sales","EU_Sales","JP_Sales","Other_Sales")) %>%
                       mutate(Region_updated = case_when(
                       Region == "NA_Sales" ~ "North America",
                       Region == "EU_Sales" ~ "Europe",
                       Region == "JP_Sales" ~ "Japan",
                       Region == "Other_Sales" ~ "Other",
                       TRUE ~ Region)) %>% group_by(Publisher,Genre) %>%
                       mutate(pub_sales = sum(Sales)) %>%
                       mutate(tooltip = paste0('Genre: ',Genre, '\n', 'Total Sales: ', pub_sales))
                       
  
top_pub <- data_set2 %>% group_by(Publisher) %>% mutate(top_sales = sum(Sales)) %>%
           select(Publisher,top_sales) %>% unique() %>% arrange(desc(top_sales)) %>%
           head(n=5) %>% select(Publisher)

bar_data_set <- data_set2 %>% filter(Publisher %in% top_pub$Publisher)

bar_plot <- ggplot(bar_data_set,aes(fill= Genre,x=Sales/1000000,y=Publisher,tooltip = tooltip)) +
  geom_bar_interactive(position="stack",stat="identity") + labs(x = 'Sales in Millions', y = 'Publishers', title = 'Top Video Game Genres by Publishers') + scale_color_brewer(palette="Set3")

ggiraph(ggobj = bar_plot)

## Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.

Sales Region Tree map

tree_data_set <- data_set %>% select(-c("Platform","Rank")) %>% unique() %>%
                 gather("Region","Sales",c("NA_Sales","EU_Sales","JP_Sales","Other_Sales")) %>%
                 group_by(Name,Region) %>%
                 select(Name,Genre,Region,Sales) %>% mutate(Region_updated= Region) %>% unique() %>%
                  mutate(Region_updated = case_when(
                       Region == "NA_Sales" ~ "North America",
                       Region == "EU_Sales" ~ "Europe",
                       Region == "JP_Sales" ~ "Japan",
                       Region == "Other_Sales" ~ "Other",
                       TRUE ~ Region))
              
d3tree2(treemap(tree_data_set,index=c("Region_updated","Genre"),vSize="Sales",title= "Sales by Region and Genre"),rootname="Sales by Region and Genre")

Top 100 Video Games

point_df <- df1 %>% group_by(Name,Platform) %>% mutate(all_sales = sum(Sales)) %>% ungroup() %>%
  mutate(tooltip = paste0(tooltip = paste0('Title: ', stringr::str_to_title(Name), 
  '\nRank: ', rank, 
  '\nSales: ', all_sales,
  '\nPlatform: ', Platform))) %>% select(rank,Platform,Name,all_sales,tooltip) %>% unique()

viz1 <- ggplot(point_df, mapping=aes(x = rank, y = all_sales/1000000, color = Platform, tooltip = tooltip)) +
  geom_point_interactive(size = 6, stat = "identity") +
  scale_x_reverse() +
  scale_color_brewer(palette = 'Set3') +
  theme_minimal() +
  labs(x = 'Rank', y = 'Sales in Millions', title = 'Sales of Top 100 Video Games') +
  theme(plot.title =element_text(hjust = .5, size = 18), text = element_text(size = 12))
ggiraph(ggobj = viz1)

  histo_df <- df3 %>% 
             group_by(Name,Region,Genre) %>% mutate(all_sales = sum(Sales)) %>% ungroup()
  viz3 <- histo_df %>% ggplot(aes(x = all_sales/1000000)) +
    geom_histogram(binwidth = 1, fill = "#A3E4D7", color = "#A3E4D7") +
  labs(x = 'Total Sales(Millions)', y = 'Count', title = 'Histogram of Sales of Top 100 Games') +
  theme(plot.title =element_text(hjust = .5, size = 18), text = element_text(size = 12))
ggiraph(ggobj = viz3)

Twitch Streams to Sales

bubble_df <- df2 %>% select(-c("Region")) %>%
    mutate(tooltip = paste0(tooltip = paste0('Title: ', stringr::str_to_title(Name),
                                             '\nGenre: ',Genre,
                                             '\nHours Watched On Twitch (Millions): ', total_hours_watched/1000000, 
                                             '\nSales (Millions): ', all_sales/1000000,
                                             '\nAverage Peak Channels: ', avg_peak_channels))) %>% unique()
  
viz2 <- ggplot(bubble_df, mapping = aes(x = total_hours_watched/1000000, y=all_sales/1000000, size = avg_peak_channels, color = Genre, tooltip = tooltip)) +
    geom_point_interactive(alpha = .7) +
    scale_size(range = c(1.4, 20), name="Average Peak Channels") +
    labs(x = 'Hours Watched On Twitch in Millions', y = 'Sales in Millions', title = 'Hours Watched on Twitch to Sales') +
    scale_color_brewer(palette = 'Set3') +
    theme_minimal() +
    theme(plot.title = element_text(hjust = .5, size = 18), text = element_text(size = 12))
ggiraph(ggobj = viz2)

Metacritic Rating Regression

mscore_data_set <- regression_data_set
                    

mscore <- ggplot(mscore_data_set,mapping= aes(x=meta_score,y=Sales/1000000)) + geom_point_interactive(aes(tooltip=meta_tooltip)) +
          geom_smooth() + scale_color_brewer(palette="Set3") +
          labs(x="Metacritic Rating (1-100)",y= "Sales in Millions",title= "Metacritic Rating Regression on Global Sales") +
          theme(plot.title = element_text(hjust = .5, size = 18), text = element_text(size = 12))

ggiraph(ggobj=mscore)

## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

mscore_histo <- regression_data_set
viz4 <- mscore_histo %>% ggplot(aes(x = meta_score)) +
    geom_histogram(binwidth = 1, fill = "#A3E4D7", color = "#A3E4D7") +
  labs(x = 'Metacritic Score', y = 'Count', title = 'Histogram of Metacritic Scores') +
  theme(plot.title =element_text(hjust = .5, size = 18), text = element_text(size = 12))
ggiraph(ggobj = viz4)

User Rating Regression

uscore_data_set <- regression_data_set
                  
uscore <- ggplot(uscore_data_set,mapping= aes(x=user_review,y=Sales/1000000)) + geom_point_interactive(aes(tooltip=user_tooltip)) + geom_smooth() + scale_color_brewer(palette="Set3") + scale_y_continuous(labels= scales::comma) +
          labs(x= "User Rating (1-100)",y= "Sales in Millions",title= "User Rating Regression on Global Sales") +
          theme(plot.title = element_text(hjust = .5, size = 18), text = element_text(size = 12))
ggiraph(ggobj=uscore)

## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

uscore_histo <- regression_data_set
viz5 <- uscore_histo %>% ggplot(aes(x = user_review)) +
    geom_histogram(binwidth = 1, fill = "#A3E4D7", color = "#A3E4D7") +
  labs(x = 'User Score', y = 'Count', title = 'Histogram of User Score') +
  theme(plot.title =element_text(hjust = .5, size = 18), text = element_text(size = 12))
ggiraph(ggobj = viz5)